Search Results (472)

Rural public spaces are crucial to the daily activities of older adults; however, limited research has examined the effects of their environmental characteristics on older adults’ spatiotemporal behavior and perception from a multisensory perspective. This study hypothesizes that composite sensory environments have significant nonlinear predictive effects on older adults’ behavior types and satisfaction. In this study, 10 sample spaces were selected in Qingdao, China. Multi-source data were collected through a two-week period of unobtrusive observation and subjective questionnaire surveys (N = 241). Multiple logistic regression was used to analyze the main effects of environmental characteristics, and an MLP model with a single hidden layer of 100 units was constructed to predict dwell time and satisfaction. The results show that, in the investigated rural context, older adults’ dominant behavior was social activity (81.12%), which mainly occurred in built spaces such as squares. Multiple logistic regression indicated that, among the various environmental factors, visual aesthetics had a statistically significant effect on behavior types (p = 0.013). The MLP model achieved prediction accuracies of 85.3% for dwell time and 93.1% for satisfaction. The key predictive variables were volume perception (100% importance), the Natural Sound Index (NSI) (92.1%), and visual aesthetics (89.3%). Subgroup heterogeneity analysis further showed that older-old adults and those with poorer health conditions were more sensitive to pavement quality and physical comfort, whereas older adults living alone or with limited household companionship were more strongly influenced by visual aesthetics and natural soundscape quality. The theoretical significance of this study lies in proposing quantitative measures of natural sound and odor indices and revealing that, in the specific northern rural built environment, the coordinated design of visual and auditory environments plays an important role in improving spatial quality. The findings provide empirical support for the age-friendly micro-renewal of rural public spaces in specific regions. However, due to the limitations of single-season data and a relatively small sample size, their generalizability needs to be further verified across regions. Full article

The reliable identification of impairment relevant to safety-critical activities remains a significant challenge for public safety, motivating the exploration of unobtrusive and widely accessible sensing technologies. This study examines the viability of utilising inertial data acquired from consumer-grade smartphones to characterise gait disturbances associated with simulated visual impairment. The study simulates intoxication-related effects using alcohol-impairment goggles and does not involve the measurement of real alcohol intoxication. Two supervised experimental protocols were conducted in which participants traversed predefined walking routes under normal conditions and while wearing alcohol-impairment simulation goggles representing five manufacturer-declared blood alcohol concentration (BAC)-related goggle conditions plus a no-goggles control condition. An initial indoor trial, conducted in a structured corridor environment, yielded limited discrimination of gait dynamics due to strong spatial and visual stabilisation cues. To address this limitation, a subsequent outdoor experiment was conducted along a 100 m path lacking prominent visual reference points, resulting in motion patterns that more closely reflect unconstrained, real-world locomotion. Tri-axial accelerometer and gyroscope signals were recorded using smartphones, followed by artefact removal, segmentation, and standardisation to ensure inter-trial comparability. The resulting curated dataset comprises 290,919 multi-channel samples derived from 96 walking trials involving 16 participants and is released as an openly accessible resource to support further research in gait analysis and classification of gait alterations associated with simulated impairment. Model evaluation was performed using an 80/20 train–test split conducted within each traversal, with training and test windows originating from the same participant and walking session. Consequently, the reported results reflect within-subject performance instead of subject-independent generalisation. Multiple deep learning architectures combining convolutional feature extraction, bidirectional long short-term memory layers, and self-attention mechanisms were systematically evaluated. Using a subject-dependent evaluation protocol, the best-performing architecture achieved an accuracy of 71.4% and a weighted F1-score of 71.5% in distinguishing gait patterns associated with different levels of simulated visual impairment. The best-performing architectures yielded classification performance consistent with exploratory, low-stakes assessment of gait alterations associated with simulated visual impairment, using accelerometer data alone. These findings illustrate the feasibility of using smartphones as auxiliary tools for exploratory, low-stakes screening or educational applications and contribute a publicly released dataset and benchmark results to facilitate methodological advancement in inertial sensor-based gait impairment analysis. Full article

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

The growing prevalence of chronic diseases, population aging, and the shift toward preventive and personalized care under Healthcare 5.0 have increased the need for continuous health monitoring beyond clinical settings. While wearable devices enable remote monitoring, their long-term use is often limited by user compliance, comfort issues, battery dependence, and disruption of daily routines. To address these limitations, unobtrusive home-based health monitoring systems have emerged, integrating sensing technologies into domestic environments and everyday objects. This review provides a system-level analysis of unobtrusive health monitoring technologies for smart homes. It examines seven major sensing approaches, including camera-, laser-, radar-, infrared-, mechanical-, bioelectrical-, and optical-based sensors, and their integration into four home environments: living areas, bathrooms, bedrooms, and home offices. For each sensing modality, the operating principles, monitored physiological parameters, representative applications, and key advantages and limitations are discussed. Overall, existing solutions reveal trade-offs among measurement accuracy, robustness in real home conditions, energy autonomy, privacy preservation, and user acceptance. Heart rate and respiratory rate are the most commonly monitored parameters, while multimodal and clinically validated systems remain limited. Although unobtrusive sensing technologies show strong potential for proactive and personalized healthcare, challenges related to accuracy, interoperability, privacy, and cost continue to hinder large-scale adoption. Full article

Sit-to-stand (STS) transitions are clinically informative indicators of functional independence and are sensitive to compensatory strategies associated with physical decline and pain. This study presents a non-contact, non-visual framework for quantitative STS assessment using a 60 GHz frequency-modulated continuous-wave (FMCW) radar in a residential setting. We developed a signal-processing pipeline that converts intermediate-frequency radar data into range–time intensity (RTI) maps, tracks dominant torso motion, and extracts temporal, kinematic, and spectral features. Experiments were conducted across two sensing orientations (subject-facing and side-facing), five mounting heights (45–153 cm), and three execution speeds, with approximately 30 repeated cycles per condition. For normal non-compensated STS transitions, radar-derived metrics reflected expected biomechanical scaling: mean full-cycle duration decreased from $23.90$ s (slow) to $13.95$ s (medium) and $7.98$ s (fast), while peak ascent velocity increased from $0.311$ m/s to $0.358$ m/s and dominant cadence increased from 0.0416 Hz to 0.125 Hz. Simulated abnormal transitions produced distinct and quantifiable deviations. Preparatory rocking introduced an additional oscillatory phase (mean rocking duration 2.36 s), prolonging the standing transition to 4.80 s and altering trajectory regularity. Across configurations, subject-facing mid-torso mounting provided the most continuous and separable STS signatures, whereas side-facing placement and extreme heights reduced effective radial motion or introduced clutter artifacts. These findings establish practical deployment guidelines and demonstrate that radar-derived STS metrics can serve as candidate digital biomarkers for unobtrusive, privacy-preserving detection of mobility decline, compensatory pain behaviors, and functional impairment in real-world home environments. Full article

Previous studies on healthy controls suggest the added value of thigh-based Electrocardiography (ECG), which collects data using sensors embedded in a toilet seat for unobtrusive signal acquisition. However, further evidence regarding its clinical feasibility is needed; with this work, we investigated three complementary aspects: signal quality, morphological correlation with standard ECG leads, and the system’s potential for heart rate variability (HRV) analysis in patients undergoing aortic valve replacement. This work was divided into two main phases. In the first, 32 healthy volunteers underwent simultaneous ECG recordings using both a standard 12-lead ECG system and the thigh-based system. Signal Quality Index (SQI) analysis revealed that 56.25% of the experimental signals were classified as excellent, and over 62.5% of recordings showed a strong correlation with Lead I of the clinical ECG. These findings extend the state of the art by further characterising the quality and relevance of the captured signals. In the second phase, two patients with severe aortic stenosis were monitored before and after surgical valve replacement. HRV metrics derived from the thigh-based ECG captured distinct autonomic responses: one patient showed significant postoperative improvement in global and parasympathetic modulation (increased SDNN, RMSSD, and Sample Entropy), while the other exhibited reduced variability and complexity, potentially indicating impaired autonomic recovery. These results highlight the feasibility of thigh-based ECG data acquisition for passive, longitudinal cardiac health monitoring in everyday environments and its applicability for pre- and postoperative autonomic assessment. Full article

Blood pressure monitoring is important for overall health assessment, yet traditional cuff-based methods are intrusive and unsuitable for continuous monitoring. This paper proposes a contactless approach for blood pressure estimation from facial videos using a bidirectional Mamba-based architecture with uncertainty quantification. Our method processes 64-frame video segments through a hierarchical 3D convolutional encoder to extract spatiotemporal features, then applies bidirectional state-space modeling to capture temporal dynamics efficiently. The model was evaluated on the Vitals for Vision (V4V) dataset, achieving mean absolute errors of 13.15 mmHg for systolic and 9.56 mmHg for diastolic blood pressure, outperforming prior methods while requiring significantly fewer computational resources than attention-based approaches. While these results do not meet clinical-grade diagnostic standards, they demonstrate the feasibility of contactless blood pressure estimation for non-clinical applications such as wellness monitoring, preliminary health screening, and continuous remote observation, where unobtrusive and computationally efficient monitoring is desirable. Full article

Accurate sleep stage classification is essential for evaluating sleep quality, yet clinical polysomnography is impractical for continuous home-based monitoring. Ballistocardiography (BCG) enables unobtrusive sleep monitoring through sensors embedded in sleep furniture; however, existing BCG-based approaches either rely on complex physiological feature extraction or employ fixed-parameter signal-to-image transformations that cannot adapt to inter-subject variability. This study proposes a learnable recurrence plot (RP) framework for three-stage sleep classification (Wake, NREM, REM) from single-channel BCG signals. The Learnable RP introduces three innovations: multi-scale phase-space reconstruction at physiologically motivated time delays (τ = 5, 10, 20), differentiable per-scale thresholds optimized end-to-end, and attention-based spatial fusion of multi-scale recurrence maps. The framework was evaluated through 10-fold stratified cross-validation across six backbone architectures using 50 overnight recordings. The Learnable RP consistently outperformed four baseline transformation methods (GAF, MTF, Classical RP, Modified RP), achieving an aggregate mean accuracy of 73.60%, with EfficientNet-B5 reaching 78.91%. and 78.91%. Statistical validation across all 24 pairwise comparisons (4 baselines × 6 backbones) confirmed consistent superiority (all p < 0.001). The proposed framework achieves competitive performance without explicit physiological feature engineering, offering a viable path toward end-to-end unobtrusive sleep monitoring. Full article

The integration of artificial intelligence with unobtrusive sensing technologies is transforming occupational health monitoring by enabling continuous, objective assessment of worker activities in real industrial environments. This study focuses on the accurate recognition of subtle motion actions within logistics workflows using multichannel optical motion-capture data. We investigate several deep learning architectures commonly employed for temporal motion analysis, including tCNN, Transformer, CNN–LSTM, and ConvLSTM. To enhance robustness and fairness across workers with varying movement styles, a subject-independent evaluation protocol is adopted, and a multiscale temporal learning strategy is explored to better capture fine-grained and low-saliency actions. Experimental results show that the proposed multiscale tCNN achieves the highest accuracy, obtaining per-class recall range between 73% and 83% and an overall accuracy of approximately 79%, consistently outperforming recurrent and attention-based architectures. These findings demonstrate the effectiveness of multiscale convolution-based temporal modeling for recognizing subtle motion actions and highlight the potential of combining optical motion capture with AI analytics to support unobtrusive, reliable occupational health monitoring in smart industry environments. Full article

Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead ($\Delta P=P_{\mathrm{active}}-P_{\mathrm{idle}}$) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

Motion sickness (MS), or kinetosis, is a condition experienced by some individuals in response to rhythmic or irregular body motion. Multiple studies have explored its neurobiological mechanisms and countermeasures, with the sensory-conflict hypothesis remaining the most accepted explanation. Heart-rate variability (HRV) and electrocardiography provide complementary autonomic nervous system perspectives that may support MS assessments. From an applied viewpoint, reliable HRV markers could enable the early detection and continuous monitoring of MS in real-world contexts, such as autonomous vehicles, where passenger comfort and safety are critical, motivating contact-free cardiac sensing for unobtrusive monitoring. This systematic review examines the value of HRV indices in MS, conducted under PRISMA guidelines across PubMed, Scopus, and the Web of Science. The included studies were grouped into four categories based on the methods used to induce MS: mechanical stimulus, real trip, visual stimulus, and virtual reality. Aggregated findings indicate that frequency–domain metrics, particularly the low frequency (LF)/high frequency (HF) ratio, HF power, and mean heart rate (mHR), are most frequently reported in relation to MS. Overall, autonomic dysregulation likely contributes to MS susceptibility, but standardized protocols are needed to validate HRV as a reliable marker. Full article

Falls represent a major health risk for older adults living independently, motivating the development of unobtrusive and privacy-preserving monitoring solutions. This study investigates whether Bluetooth Low Energy (BLE) 6.0 Channel Sounding (CS) can support device-free fall detection using low-complexity signal representations suitable for residential deployment. The proposed system employs two BLE nodes performing periodic channel sounding, from which only scalar distance estimates are extracted. Time-domain and temporal-dynamic features are computed from sliding windows of the distance signal and used for supervised classification. Three widely used classifiers—Support Vector Machine with radial basis function kernel, Random Forest, and gradient-boosted decision trees (XGBoost)—are evaluated under both a default operating point and a sensitivity-first regime achieved through validation-based decision threshold adjustment, reflecting the higher cost of missed fall detections in assisted living scenarios. Experiments conducted in a furnished indoor environment with six participants performing realistic fall and non-fall scenarios demonstrate strong window-level sensitivity under subject-independent evaluation, with XGBoost providing the most favorable sensitivity–specificity balance. Under sensitivity-first operation, very high recall is achieved at the expense of increased false alarms. Given the limited dataset and single-environment setting, the reported results should be interpreted as a proof-of-concept demonstration of feasibility rather than definitive large-scale performance. The findings suggest that BLE CS captures motion-relevant signal variations that may support practical fall detection while maintaining low deployment complexity and privacy preservation. Full article

As artificial intelligence (AI) systems are increasingly deployed in real-world applications, concerns regarding the unauthorized use of copyrighted images during model training have become more pronounced. In particular, both generative and discriminative models may implicitly internalize distinctive visual patterns from copyrighted data, leading to potential ethical and legal risks even after data removal. In this study, we propose a practical copyright protection framework, termed the Predictable-Image Solution (PIS), which aims to disrupt the learning of copyrighted visual features during the training process. PIS leverages Layer-wise Relevance Propagation (LRP) to identify image regions that contribute positively to a model’s prediction and selectively modifies these regions using non-copyrighted visual substitutes, such as textures or benign image patterns. By targeting semantically influential regions rather than applying global perturbations, the proposed approach effectively interferes with feature extraction while preserving the perceptual quality and overall visual structure of the original image. Extensive experiments conducted on multiple pre-trained image classification models demonstrate that PIS consistently degrades classification performance on protected images, while maintaining high visual similarity as measured by perceptual metrics. These results indicate that PIS offers an effective, model-agnostic, and visually unobtrusive solution for mitigating unauthorized exploitation of copyrighted images in practical AI training scenarios. Full article